Introduction to R shiny package

Hakan Turgay & Bertan Taylan

Chapter 1

Motivation, Basic R-Shiny Package and Example

Motivation

Scientists mostly use R to process their analyzes
Presenting/Sharing their findings are usually done in static format
Problem : They cannot present additional questions directly
Simple idea: Immigrating ratios from a specific region of Turkey due to years and also forecasts about these ratios (e.g. Journalist)

What is Shiny package?

Shiny is an R package that makes it easy to build interactive web applications (apps) straight from R. This lesson will get you started building Shiny apps right away.
Build useful web applications with only a few lines of code—no JavaScript required.
Shiny applications are automatically “live” in the same way that spreadsheets are live. Outputs change instantly as users modify inputs, without requiring a reload of the browser.
Shiny user interfaces can be built entirely using R, or can be written directly in HTML, CSS, and JavaScript for more flexibility.
Pre-built output widgets for displaying plots, tables, and printed output of R objects.

Basically

R Shiny = R + interactivity + web made easy

Install Shiny Package

If you still haven’t installed the Shiny package, open an R session, connect to the internet, and run

install.packages("shiny")

First App in Shiny

library(shiny)
runExample("01_hello")

Code Output

More Example

runExample("01_hello")      # a histogram
runExample("02_text")       # tables and data frames
runExample("03_reactivity") # a reactive expression
runExample("04_mpg")        # global variables
runExample("05_sliders")    # slider bars
runExample("06_tabsets")    # tabbed panels
runExample("07_widgets")    # help text and submit buttons
runExample("08_html")       # Shiny app built from HTML
runExample("09_upload")     # file upload wizard
runExample("10_download")   # file download wizard
runExample("11_timer")      # an automated timer

Chapter 2

Structure of a Shiny App

Main Structure

ui: Nested R functions that assemble an HTML user interface for the app
server : A function with instructions on how to build and rebuild the R objects displayed in the UI
shinyApp : Combines ui and server into a functioning app
Save the template as app.R
a call to the shinyApp function

    library(shiny)
    
    ui <- fluidPage(
     
    )
    
    server <- function(input, output, session) {
      
    }
    
    shinyApp(ui, server)

Alternative Approach

kmeans

Say Hello with Shiny

library(shiny)
ui <- fluidPage(
    "Hello Shiny Package"
    )

server <- function(input, output) {
    
}

shinyApp(ui = ui, server = server)

Code Output

working code:

Chapter 3

Inputs and Outputs

Overview

Build your app around inputs and outputs

kmeans

Input Syntax

kmeans

Input

sliderInput(inputId = "num",
  label = "Choose a number",
  value = 25, min = 1, max = 100)

library(shiny)
ui <- fluidPage(
    sliderInput(inputId = "num",
                label = "Choose a number",
                value = 25, min = 1, max = 100)
    )

server <- function(input, output) {
    
}

shinyApp(ui = ui, server = server)

Code Output

Entegration Input and Output

Use 3 rules to write the server function

server <- function(input, output) {




}

1 - Save objects to display to output$


server <- function(input, output) {  
  output$hist <- # code
}

kmeans

2 - Build objects to display with render*()

[ING] UI tarafinda bulunan ‘hist’ (histogram) objesi server tarafindaki ‘RenderPlot’ methodu ile uretilecek . Not: hist ve RenderPlot vurgulanacak ..


server <- function(input, output) {  
  output$hist <- renderPlot({

   #code

  }) 
}

Render Method

First Rule


server <- function(input, output) {
  output$hist <- renderPlot({

    title <- "100 random normal values"           

    hist(rnorm(100), main = title) 

  }) 
}

3 - Access input values with input$

server <- function(input, output) {  
  
  output$hist <- renderPlot({    
  hist(rnorm(input$num))  
  
  }) 

}

First Rule

…and more “Render Method” example

First Rule

Result (Reactivity)

Reactivity automatically occurs whenever you use an input value to render an output object

library(shiny)
ui <- fluidPage(
    sliderInput(inputId = "num",
                label = "Choose a number",
                value = 25, min = 1, max = 100),
    plotOutput("hist")
    )

server <- function(input, output) {
    output$hist <- renderPlot({
        hist(rnorm(input$num))
    })
}

shinyApp(ui = ui, server = server)

Code Output

working code:

Chapter 4

Using Checkbox in Shiny

Reviewing an Example

library(shiny)
runExample("02_text")

Starting to Code Analysis

Change code

library(shiny)

ui <- fluidPage(
  titlePanel("Shiny Text"),
  sidebarLayout(
    
    sidebarPanel(
      
      checkboxGroupInput("dataset", "Choose a dataset:",
                         c("rock", "pressure", "cars"),
                         selected = "rock"),
                         
      numericInput(inputId = "obs",
                   label = "Number of observations to view:",
                   value = 10)
    ),
    
    mainPanel(
      verbatimTextOutput("summary"),
      tableOutput("view")
    )
  )
)

Result

working code:

Chapter 5

Improving the Design

Design Part

Assemble UI with HTML/CSS/… widgets
Adjustment of the layout scheme

Chapter 6

Implementations in Shiny: JavaScript, HTML and CSS

Using HTML, CSS and Javascript in R Code

In R, HTML elements can be defined by tags keyword.

ui <− fluidPage (
tags$h1(”R Shiny Introduction”) ,
tags$hr () ,
tags$br () ,
tags$p(strong(”Istanbul Technical University”)),
tags$p(em(”Mathematical Engineering”)),
tags$a(href=”https://www.itu.edu.tr”, ”Website”))
server <− function(input , output){} > 
shinyApp(ui = ui , server = server)

…and more Html Tag

html tag

External file import (html, css , js ext)

Method of importing depends on type of the file;

To include a CSS file

use includeCSS() or
1. Place the file in the www subdirectory
2. Link to it with:

tags$head(tags$link(rel = "stylesheet",
type = "text/css", href = "<file name>"))

To include JavaScript

use includeScript() or
1. Place the file in the www subdirectory
2. Link to it with:

tags$head(tags$script(src = "<file name>"))

To include HTML file

includeHTML("include.html")

Layout structure of the UI

Combine multiple elements into a ”single element” that has its own properties with a panel function

  ui <-
    fluidPage (
    wellPanel(
    numericInput(inputId = 'n', 'Sample size', value = 25),
    submitButton () ) , plotOutput(outputId = 'hist'))
  server <- function(input , output ){
  } > 
  shinyApp(ui = ui , server = server)

working code:

Chapter 7

Introduction to HTMLWidgets Package

Overview

This package builds a framework for creating R bindings to JavaScript libraries. HTMLWidgets can be
- Used at the R console for data analysis
- Embedded within R Markdown documents
- Incorporated into Shiny web applications

Some most-used HTMLWidget Examples

kmeans

leaflet

kmeans

dygraph

Scaffolding

To create a new widget you can call the scaffoldWidget function to generate the basic structure for your widget. This function will:

Create the .R, .js, and .yaml files required for your widget
Tip: If provided, take a Bower package (which is a package manager for web) name and automatically download the JavaScript library (and its dependencies) and add the required entries to the .yaml file. This method is highly preferrable because it guarantees that you get started with the right file structure.

MyWidget

We want to create a widget named ‘mywidget’ in a new package of the same name:

devtools::create("mywidget")               # create package using devtools
setwd("mywidget")                          # navigate to package dir
htmlwidgets::scaffoldWidget("mywidget")    # create widget scaffolding
devtools::install()                        # install the package so we can try it

This creates a simple widget that takes a single text argument and displays that text within the widgets HTML element. You can try it like this:

library(mywidget)
mywidget("hello, world")

This is the most minimal widget possible and doesn’t yet include a JavaScript library.

Chapter 8

Case Studies

Case Study: Predicted Deaths From Lung Disease By Using Dygraph Package

Ui part of the code:

ui <- fluidPage(
  
  titlePanel("Predicted Deaths from Lung Disease (UK)"),
  
  sidebarLayout(
    sidebarPanel(
      numericInput("months", label = "Months to Predict", 
                   value = 72, min = 12, max = 144, step = 12),
      selectInput("interval", label = "Prediction Interval",
                  choices = c("0.80", "0.90", "0.95", "0.99"),
                  selected = "0.95"),
      checkboxInput("showgrid", label = "Show Grid", value = TRUE),
      hr(),
      div(strong("From: "), textOutput("from", inline = TRUE)),
      div(strong("To: "), textOutput("to", inline = TRUE)),
      div(strong("Date clicked: "), textOutput("clicked", inline = TRUE)),
      div(strong("Nearest point clicked: "), textOutput("point", inline = TRUE)),
      br(),
      helpText("Click and drag to zoom in (double click to zoom back out).")
    ),
    mainPanel(
      dygraphOutput("dygraph")
    )

Server part of the code

server <- function(input, output) {
  
  predicted <- reactive({
    hw <- HoltWinters(ldeaths)
    predict(hw, n.ahead = input$months, 
            prediction.interval = TRUE,
            level = as.numeric(input$interval))
  })
  output$dygraph <- renderDygraph({
    dygraph(predicted(), main = "Predicted Deaths/Month") %>%
      dySeries(c("lwr", "fit", "upr"), label = "Deaths") %>%
      dyOptions(drawGrid = input$showgrid)
  })
  output$from <- renderText({
    strftime(req(input$dygraph_date_window[[1]]), "%d %b %Y")      
  })
  output$to <- renderText({
    strftime(req(input$dygraph_date_window[[2]]), "%d %b %Y")
  })
  output$clicked <- renderText({
    strftime(req(input$dygraph_click$x), "%d %b %Y")
  })
  output$point <- renderText({
    paste0('X = ', strftime(req(input$dygraph_click$x_closest_point), "%d %b %Y"), 
           '; Y = ', req(input$dygraph_click$y_closest_point))
  })

Result

Case Study: Migration By Using Leaflet Package

We used Aegean Boat Report’s data from: https://aegeanboatreport.com/reports/
By using Excel, we shaped this data to make it useful for our research.

Using Leaflet

For creating a map we used Leaflet library.
Leaflet is a open source JavaScript library for creating interactive maps.
It can be implemented and used in Shiny easily.
Leaflet gives the opportunity of adding informational popups to particular part of maps.

Result

Case Study: Childlessness and Gender Gap By Using Ggraph Package

In this section, we will build interactive world maps and show these in the form of an Shiny app.
At first, we’re going to start with importing, exploring and cleaning the data that datasets we’re going to use. Since the data sets are dirty, we have to get them in a better shape to make more useful for us.
Final forms of our datasets will be like this:

Creating a function to build map

Next, it’s time to define the function that we’ll use for building our world maps. The inputs to this function are the merged data frame, the world data containing geographical coordinates, and the data type, period and indicator the user will select in the R Shiny app.

worldMaps <- function(df, world_data, data_type, period, indicator){
 
 # Function for setting the aesthetics of the plot
 my_theme <- function () { 
   theme_bw() + theme(axis.text = element_text(size = 14),
                      axis.title = element_text(size = 14),
                      strip.text = element_text(size = 14),
                      panel.grid.major = element_blank(), 
                      panel.grid.minor = element_blank(),
                      panel.background = element_blank(), 
                      legend.position = "bottom",
                      panel.border = element_blank(), 
                      strip.background = element_rect(fill = 'white', colour = 'white'))
 }

Function Continues:

  # Select only the data that the user has selected to view
  plotdf <- df[df$Indicator == indicator & df$DataType == data_type & df$Period == period,]
  plotdf <- plotdf[!is.na(plotdf$ISO3), ]
  # Add the data the user wants to see to the geographical world data
  world_data['DataType'] <- rep(data_type, nrow(world_data))
  world_data['Period'] <- rep(period, nrow(world_data))
  world_data['Indicator'] <- rep(indicator, nrow(world_data))
  world_data['Value'] <- plotdf$Value[match(world_data$ISO3, plotdf$ISO3)]
  
  # Create caption with the data source to show underneath the map
  capt <- paste0("Source: ", ifelse(data_type == "Childlessness", "United Nations" , "World Bank"))
 # Specify the plot for the world map
  library(RColorBrewer)
  library(ggiraph)
  g <- ggplot() + 
    geom_polygon_interactive(data = world_data, color = 'gray70', size = 0.1,
                                    aes(x = long, y = lat, fill = Value, group = group, 
                                        tooltip = sprintf("%s<br/>%s", ISO3, Value))) + 
    scale_fill_gradientn(colours = brewer.pal(5, "RdBu"), na.value = 'white') + 
    labs(fill = data_type, color = data_type, title = NULL, x = NULL, y = NULL, caption = capt) + 
    my_theme()
  return(g)
}

Last Step: Building the Shiny App

Now we put our data in a good shape and world mapping function ready and specified.
Let’s start with UI.

# Define the UI
ui = fluidPage(
  
  # App title
  titlePanel("Childlessness and Gender Gap Index Data"),
  
  # Sidebar layout with input and output definitions
  sidebarLayout(
    
    # Sidebar panel for inputs 
    sidebarPanel(
      
      # First input: Type of data
      selectInput(inputId = "data_type",
                  label = "Choose the type of data you want to see:",
                  choices = list("Childlessness" = "Childlessness", "Gender Gap Index" = "Gender Gap Index")),
      
      # Second input (choices depend on the choice for the first input)
      uiOutput("secondSelection"),
      
      # Third input (choices depend on the choice for the first and second input)
      uiOutput("thirdSelection")
    ),

Ui Continues with Main Panel:

 # Main panel for displaying outputs
    mainPanel(
      
      # Hide errors
      tags$style(type = "text/css",
                 ".shiny-output-error { visibility: hidden; }",
                 ".shiny-output-error:before { visibility: hidden; }"),
      
      # Output: interactive world map
      girafeOutput("distPlot")
      
    )
  )
)

Server side of the app

# Define the server
server = function(input, output) {
  
  # Create the interactive world map
  output$distPlot <- renderGirafe({
    ggiraph(code = print(worldMaps(df, world_data, input$data_type, input$period, input$indicator)))
  })
  
  # Change the choices for the second selection on the basis of the input to the first selection
  output$secondSelection <- renderUI({
    choice_second <- as.list(unique(df$Period[which(df$DataType == input$data_type)]))
    selectInput(inputId = "period", choices = choice_second,
                label = "Choose the period for which you want to see the data:")
  })
  
  # Change the choices for the third selection on the basis of the input to the first and second selections
  output$thirdSelection <- renderUI({
    lab <- ifelse(input$data_type == "Childlessness", "age group", "indicator")
    choice_third <- as.list(unique(df$Indicator[df$DataType == input$data_type & df$Period == input$period]))
    selectInput(inputId = "indicator", choices = choice_third,
                label = paste0("Choose the type of ", lab, " you want to explore:"))
  })
}

Result

example

Case Study: Earthquake Map By Using Leaflet Package

We used USGS’s data from: https://earthquake.usgs.gov/earthquakes/search/
By using filtering options, we took the data of a specific time period for specific locations in order to use in our research.

Distinguishing Depths

library(shiny)
library(leaflet)
library(dplyr)
library(leaflet.extras)

data <- read.csv("./data/data.csv")
country <- data["place"]

data$depth_type <-      ## 
    ifelse(
        data$depth <= 70,
        "shallow",
        ifelse(
            data$depth <= 300 | data$depth > 70,
            "intermediate",
            ifelse(data$depth > 300, "deep", "other")

Forming the UI

ui <- fluidPage(mainPanel(
    #this will create a space for us to display our map
    leafletOutput(outputId = "mymap"),
    #this allows us to put the checkmarks on top of the map to
    #allow people to view earthquake depth or overlay a heatmap.
    absolutePanel(
        top = 250,
        left = 30,
        checkboxInput("markers", "Depth", FALSE),
        checkboxInput("heat", "Heatmap", FALSE)
    )
))

Server part

server <- function(input, output, session) {
    #define the color palette for the magnitude of the earthquake
    pal <- colorNumeric(
        palette = c(
            'gold',
            'orange',
            'dark orange',
            'orange red',
            'red',
            'dark red'
        ),
        domain = data$mag
    )
    #define the color of for the depth of the earthquakes
    pal2 <- colorFactor(palette = c('blue', 'yellow', 'red'),
                        domain = data$depth_type)

Creating the Map

output$mymap <- renderLeaflet({
leaflet(data) %>%
  addTiles() %>%
  addFullscreenControl(position = "topleft", pseudoFullscreen = TRUE) %>%
    setView(lng = 35, lat = 42, zoom = 5)  %>% #setting the view over
      addTiles() %>%
      addCircles(
          data = data,
          lat = ~ latitude,
          lng = ~ longitude,
          weight = 0.5,
          radius = ~ sqrt(mag) * 15000,
          popup = ~ as.character(mag),
          label = ~ as.character(paste0("Magnitude: ", sep = " ", mag)),
          color = ~ pal(mag),
          fillOpacity = 0.5

Making Checkboxes Dynamic With observe Function

observe({
        proxy <- leafletProxy("mymap", data = data)
        proxy %>% clearMarkers()
        if (input$markers) {
            proxy %>% addCircleMarkers(
                stroke = FALSE,
                color = ~ pal2(depth_type),
                fillOpacity = 0.2,
                label = ~ as.character(paste0("Magnitude: ", sep = " ", mag))
            ) %>%
                addLegend(
                    "bottomright",
                    pal = pal2,
                    values = data$depth_type,
                    title = "Depth Type",
                    opacity = 1
                )
        }
        else {
            proxy %>% clearMarkers() %>% clearControls()
        }

Map Adjustments with Using leafletproxy Function

observe({
        proxy <- leafletProxy("mymap", data = data)
        proxy %>% clearMarkers()
        if (input$heat) {
            proxy %>%  addHeatmap(
                lng =  ~ longitude,
                lat =  ~ latitude,
                intensity = ~ mag,
                blur =  10,
                max = 0.05,
                radius = 15
            )
        }
        else{
            proxy %>% clearHeatmap()
        }

Result

Introduction to R shiny package

Hakan Turgay & Bertan Taylan

Chapter 1 Motivation, Basic R-Shiny Package and Example

Motivation

What is Shiny package?

Basically

Install Shiny Package

First App in Shiny

More Example

Chapter 2 Structure of a Shiny App

Main Structure

Alternative Approach

Say Hello with Shiny

Chapter 3 Inputs and Outputs

Overview

Input Syntax

Input

Code Output

Entegration Input and Output

1 - Save objects to display to output$

2 - Build objects to display with render*()

Render Method

3 - Access input values with input$

…and more “Render Method” example

Result (Reactivity)

Code Output

Chapter 4 Using Checkbox in Shiny

Reviewing an Example

Starting to Code Analysis

Change code

Result

Chapter 5 Improving the Design

Design Part

Chapter 6 Implementations in Shiny: JavaScript, HTML and CSS

Using HTML, CSS and Javascript in R Code

…and more Html Tag

External file import (html, css , js ext)

To include a CSS file

To include JavaScript

To include HTML file

Layout structure of the UI

Chapter 7 Introduction to HTMLWidgets Package

Overview

Creating a Widget

Some most-used HTMLWidget Examples

Creating a widget step-by-step

Requirements

Scaffolding

MyWidget

Chapter 8 Case Studies

Case Study: Predicted Deaths From Lung Disease By Using Dygraph Package

Server part of the code

Result

Case Study: Migration By Using Leaflet Package

Using Leaflet

Result

Case Study: Childlessness and Gender Gap By Using Ggraph Package

Creating a function to build map

Function Continues:

Last Step: Building the Shiny App

Ui Continues with Main Panel:

Server side of the app

Result

Case Study: Earthquake Map By Using Leaflet Package

Distinguishing Depths

Forming the UI

Server part

Creating the Map

Making Checkboxes Dynamic With observe Function

Map Adjustments with Using leafletproxy Function

Result

Chapter 1

Motivation, Basic R-Shiny Package and Example

Chapter 2

Structure of a Shiny App

Chapter 3

Inputs and Outputs

Chapter 4

Using Checkbox in Shiny

Chapter 5

Improving the Design

Chapter 6

Implementations in Shiny: JavaScript, HTML and CSS

Chapter 7

Introduction to HTMLWidgets Package

Chapter 8

Case Studies